Objectives: Pollutants emitted from ships pose significant environmental challenges, particularly to air quality in port-adjacent regions, while greenhouse gas (GHG) emissions exacerbate global warming. Existing emission inventories for Tianjin Port often overlook the Domestic Emission Control Areas (DECA) policy. Additionally, forecasting models relying on multiple data sources face practical constraints. Methods: This study compiled a high spatial-temporal resolution emission inventory of pollutants and GHGs from ships in Tianjin Port in 2018, using AIS data and DECA policies. Four types of time series models—based on Transformer, MLP, TCN, and RNN—were employed to predict emissions. Results: The results showed that \(\text {SO}_\text {X}\) and CO \(_2\) are the primary pollutants and GHGs, with oil tankers, dry bulk carriers, and container ships being the main emission sources. Emissions from the main engine accounted for over 80% in channels, while auxiliary engine and boiler emissions were lower. However, in berths and anchorages, main engine emissions were almost negligible. Anchoring and docking contributed significantly, with emissions from these areas accounting for 94.94% of total emissions. Time series prediction results indicated that SCINet outperformed other models in low-value emission prediction. Conclusions: This study aligned with the DECA policy to develop an emissions inventory for Tianjin Port in 2018, examining the emission patterns of pollutants and GHGs from multiple perspectives. It also achieved emissions forecasting under a single data source condition.